LibIsolationForest

Description

This project contains Rust, C++, Julia, and python implementations of the Isolation Forest algorithm. Isolation Forest is an anomaly detection algorithm based around a collection of randomly generated decision trees. For a full description of the algorithm, consult the original paper by the algorithm's creators:

https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf

Python Example

The python implementation can be installed via pip:

pip install IsolationForest

This is a short code snipet that shows how to use the Python version of the library. You can also read the file test.py for a complete example. As the library matures, I'll add more test examples to this file.

from isolationforest import IsolationForest

forest = IsolationForest.Forest(num_trees, sub_sampling_size)

sample = IsolationForest.Sample("Training Sample 1")
features = []
features.append({"feature 1": feature_1_value})
# Add more features to the sample...
features.append({"feature N": feature_N_value})
sample.add_features(features)
# Add the features to the sample.
forest.add_sample(sample)
# Add more samples to the forest...

# Create the forest.
forest.create()

sample = IsolationForest.Sample("Test Sample 1")
features = []
features.append({"feature 1": feature_1_value})
# Add more features to the sample...
features.append({"feature N": feature_N_value})
# Add the features to the sample.
sample.add_features(features)

# Score the sample.
score = forest.score(sample)
normalized_score = forest.normalized_score(sample)

Rust Example

More examples of how to use the Rust version of the library can be found in lib.rs. As the library matures, I'll add more test examples to this file.

let file_path = "../data/iris.data.txt";
let file = match std::fs::File::open(&file_path) {
    Err(why) => panic!("Couldn't open {} {}", file_path, why),
    Ok(file) => file,
};

let mut reader = csv::Reader::from_reader(file);
let mut forest = crate::isolation_forest::Forest::new(10, 10);
let training_class_name = "Iris-setosa";
let mut training_samples = Vec::new();
let mut test_samples = Vec::new();
let mut avg_control_set_score = 0.0;
let mut avg_outlier_set_score = 0.0;
let mut avg_control_set_normalized_score = 0.0;
let mut avg_outlier_set_normalized_score = 0.0;
let mut num_control_tests = 0;
let mut num_outlier_tests = 0;
let mut rng = rand::thread_rng();
let range = Uniform::from(0..10);

for record in reader.records() {
    let record = record.unwrap();

    let sepal_length_cm: f64 = record[0].parse().unwrap();
    let sepal_width_cm: f64 = record[1].parse().unwrap();
    let petal_length_cm: f64 = record[2].parse().unwrap();
    let petal_width_cm: f64 = record[3].parse().unwrap();
    let name: String = record[4].parse().unwrap();

    let mut features = crate::isolation_forest::FeatureList::new();
    features.push(crate::isolation_forest::Feature::new("sepal length in cm", (sepal_length_cm * 10.0) as u64));
    features.push(crate::isolation_forest::Feature::new("sepal width in cm", (sepal_width_cm * 10.0) as u64));
    features.push(crate::isolation_forest::Feature::new("petal length in cm", (petal_length_cm * 10.0) as u64));
    features.push(crate::isolation_forest::Feature::new("petal width in cm", (petal_width_cm * 10.0) as u64));

    let mut sample = crate::isolation_forest::Sample::new(&name);
    sample.add_features(&mut features);

    // Randomly split the samples into training and test samples.
    let x = range.sample(&mut rng) as u64;
    if x > 5 && name == training_class_name {
        forest.add_sample(sample.clone());
        training_samples.push(sample);
    }
    else {
        test_samples.push(sample);
    }
}

// Create the forest.
forest.create();

// Use each test sample.
for test_sample in test_samples {
    let score = forest.score(&test_sample);
    let normalized_score = forest.normalized_score(&test_sample);

    if training_class_name == test_sample.name {
        avg_control_set_score = avg_control_set_score + score;
        avg_control_set_normalized_score = avg_control_set_normalized_score + normalized_score;
        num_control_tests = num_control_tests + 1;
    }
    else {
        avg_outlier_set_score = avg_outlier_set_score + score;
        avg_outlier_set_normalized_score = avg_outlier_set_normalized_score + normalized_score;
        num_outlier_tests = num_outlier_tests + 1;
    }
}

// Compute statistics.
if num_control_tests > 0 {
    avg_control_set_score = avg_control_set_score / num_control_tests as f64;
    avg_control_set_normalized_score = avg_control_set_normalized_score / num_control_tests as f64;
}
if num_outlier_tests > 0 {
    avg_outlier_set_score = avg_outlier_set_score / num_outlier_tests as f64;
    avg_outlier_set_normalized_score = avg_outlier_set_normalized_score / num_outlier_tests as f64;
}

println!("Avg Control Score: {}", avg_control_set_score);
println!("Avg Control Normalized Score: {}", avg_control_set_normalized_score);
println!("Avg Outlier Score: {}", avg_outlier_set_score);
println!("Avg Outlier Normalized Score: {}", avg_outlier_set_normalized_score);

C++ Example

An example of how to use the C++ version of the library can be found in main.cpp. As the library matures, I'll add more test examples to this file.

Julia Example

An example of how to use the Julia version of the library can be found in test.jl. As the library matures, I'll add more test examples to this file.

Version History

1.0

• Initial version.

1.1

• Added normalized scores.
• Updated random number generation in rust, because it changed again.

License

This library is released under the MIT license, see LICENSE for details.